Method and apparatus for obscuring features of an image

ABSTRACT

A method and system that obscures image features in a region of interest designated by a user. An operator designates a special shaped region of interest and a computer convolves a specially designed kernel with the image data in the region. The kernel is designed to pull features outside the region into the region while combining the information within the region with the information pulled from outside. The kernel can be designed to correspond to the shape of the region for efficient computation. Once the region of interest is obscured the image is printed resulting in a print that does not include undesirable features.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to a system for automaticallyobscuring undesirable features in an image region which system combinesimage convolution and erosion to pull features outside the region intothe region using a specially designed kernel, a shaped region ofinterest adapted to reduce artifacts while obscuring undesirablefeatures and local information.

2. Description of the Related Art

Image processing techniques which improve the appearance of images arewell known. Methods for the global reduction of image information in animage are also well known. For example, an image can be made less sharpby blurring the entire image with a convolution whose kernel elementsare chosen to suit the particular need. Information describing an imagecan also be produced with the application of algorithms which computevarious features within an image. For example, successive applicationsof erosion and dilation operations can be used to extract informationpertaining to the structure of features within an image.

Techniques for the local enhancement of image information are also wellknown. Such techniques seek to improve the apparent quality of an imagein a restricted area or attempt to derive information about an object inan image.

There are also many standard methods for removing image informationwhich rely on operator interaction. Image editing software tools provideair brushing capability, cloning operations and the like to remove imagefeatures directly under the operator's artistic control. Such methodsare not automated and all require the supervision of the operator.

Automatic operations which endeavor to effectively obscure portions ofan image (indicated by an operator) while leaving the rest of the imageunaffected are more difficult (and less sophisticated). Such operationsmust avoid destroying the overall appearance of the image or its imagequality. At the same time, the obscured image portion must not beobvious to the eye and, when more closely examined, should appear as anormal part of the image but without the undesirable features.

What is needed is an automatic operation that merges information outsidea region with information inside the region to obscure undesirablefeatures while retaining some of the characteristics of the informationin the region and being pleasing to the eye.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a method for thelocal obscuration of objectionable features within an image withoutdegrading the overall quality of the image, creating obtrusive artifactsor creating perceptible discontinuities within the image.

It is another object of the present invention to allow a user todesignate regions of an image which include undesirable information andto process the regions to obscure the undesirable features in a waypleasing to the eye.

It is also an object of the present invention to provide a system thatuses a shape of a region to be obscured that improves the perceivedquality of the final image by reducing processing artifacts.

It is a further object of the present invention to provide a kernel thatimproves the efficiency of the obscuration process.

It is an additional object of the present invention to merge informationoutside a region with information inside the region to obscureundesirable features in the information in the region.

The above objects can be attained by a method and system that obscuresimage features in a region of interest designated by a user. The processincludes designation by an operator using a computer of a special shapedregion of interest and the automatic convolution by the computer using aspecially designed kernel with the image data. The kernel is designed topull features outside the border of the region into the region makingthe region blend naturally with the surroundings while not completelyeliminating the image details within the region. The kernel is alsodesigned for efficient computation requiring only adds and shifts. Thekernel is applied iteratively from the border of the region to thecenter. Once the region of interest is obscured the image will notinclude undesirable features and can be made available for viewing orprinting.

These together with other objects and advantages which will besubsequently apparent, reside in the details of construction andoperation as more fully hereinafter described and claimed, referencebeing had to the accompanying drawings forming a part hereof, whereinlike numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the typical hardware components of the present invention;

FIG. 2 depicts the steps of the process of the present invention;

FIG. 3 illustrates an image to which the invention is to be applied;

FIG. 4 depicts an area being processed;

FIGS. 5A-5D illustrate kernels applied to the image of FIG. 4;

FIG. 6 depicts a first processing level;

FIG. 7A illustrates a kernel applied to the image;

FIG. 7B depicts processing results;

FIG. 8 illustrates a first level of processed results;

FIG. 9 illustrates a second level;

FIG. 10 depicts a final level;

FIGS. 11-13 depict different final levels;

FIGS. 14A-14D depict reduced kernels;

FIG. 15 depicts an elliptical region of interest;

FIGS. 16A and 16B illustrate the image region digitally;

FIGS. 17A-17D and 18A-18D illustrate kernels applied to the ellipticalregion;

FIG. 19 depicts pixels to be processed;

FIG. 20 illustrates the results of processing the pixels of FIG. 19;

FIG. 21 depicts the procedure for selecting kernels;

FIG. 22 illustrates the processing results for the entire first level;

FIG. 23 illustrates two levels of processing;

FIG. 24 illustrates three levels of processing; and

FIG. 25 depicts all pixels processed.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention as illustrated in FIG. 1 includes a camera 10,such as a the Eastman Kodak Co. DCS200 camera, which captures an image12 of about 4 megabytes that includes features that are undesirable.Although a camera image source is shown the image source could be otherdevices such as an image storage device like a compact disk. The image12 once it is received is stored in a computer 14, such as the SUNSPARKworkstation from Sun Microsystems or the Kodak EktronBOSS processorhaving matrix process accelerators, and used to drive a display 16. Anoperator 18 through a conventional input process and device 20, such asa mouse, designates the location and size of a region of interest in theimage 12 in which details are to be obscured. The region designation isstored in a location 22 of the memory of the computer 14. Once all theregions that need to be obscured within the image 12 are designated, anobscuration process 24 using the region designation 22 and a kernel 26automatically performs the obscuration using a conventional convolutiontechnique in which the kernel is iteratively applied from the outside tothe center to obscure the details in the image 12 in the region ofinterest. Once the image processing is complete the image 12 may beviewed on the display 16 or output to an output device 26, such as aprinter like the Kodak XL thermal printer.

The iterative method mentioned above provides for the efficientobscuration of image features using a related family of two-dimensionalkernels which are applied to the border of the image feature area firstand then successively moved to the center of the image feature area. Thekernel combines information from the border of the obscured area withinformation within the obscured area. The construction of the kernel issuch that the application of the kernel to the image is extremelyefficient computationally. The features of the kernel which improveefficiency and image quality include: making all element values a powerof two, so that only shifts (instead of multiplies) and adds arerequired; making the sum of the kernel elements a power of two; pixelsadjacent to the target pixel at the same level not contributing to theobscured pixel element values; requiring a minimum number of shifts; thekernel shape matching the shape of the region being obscured; trackingthe position of kernel elements with respect to data is simple;minimizing the number of intermediate copies of data by providing thatthe original can be overwritten; and supporting extensive parallelprocessing.

The automatic method for obscuring image features of the presentinvention does so without degrading the overall image quality, creatingimage artifacts, or creating perceptible discontinuities within theimage. The method, as illustrated in FIG. 2, can start with the capture40 of an image which includes one or more undesirable features. The onlyoperator 18 input then required is to designate or indicate 42 a regionof interest requiring obscuration. A simplified, artificial image 80 isshown in FIG. 3. The feature which will be obscured within this image isthe ball 82 in the hand 83 at the end of the left arm 84 of the humanFIG. 86 on the right side of the artificial image 80.

The area to be obscured is selected as being within a rectangle 88 bythe operator 18. Such a rectangle is shown in FIG. 3, is drawn with adotted line, and completely surrounds or encloses the area to beobscured. Once the area of interest is selected, the operator's task iscomplete and the processing proceeds automatically with the application44 of the kernel to the image.

The area to be obscured can be digitally, rather than pictorially,represented by FIG. 4. In this figure, the background has pixel valuesin the zero to ten range, the hand 83 has values in the 40 to 70 range,and the ball or circle 82 has values in the 130 to 160 range. The areawithin the dark bordered box 90 is to be obscured.

Four separate kernels are used to obscure the region of interest eachwith a rectangular shape matching the rectangular shape of the region.Each kernel is used on one of the four sides of the rectangular regionof interest. Alternatively, the kernels can be considered as the samekernel but rotated when applied to each of the four sides. That is, eachkernel is symmetric inside the kernel about a normal to the surface andeach kernel is rotationally symmetric. That is, if the kernel is rotatedand applied to the same data the same result will be produced. The fourkernels are shown in FIGS. 5A-5D. Each kernel includes an element forthe pixel of interest and for pixels exterior to the pixel of interestand exterior to the current level and which are considered complete orhave had their processing, if any completed. The obscuration processproceeds iteratively, from the outside layers, or levels, of theregion-of-interest to the inside. The first level, as illustrated inFIG. 6 is composed of the rows and columns touching the inside of thedark border and is the first to be processed. The second level is therows and columns just inside the first level and so on. Each level isprocessed before the next, although some computations may be performedconcurrently where there is no arithmetic dependency and the image canbe divided into tiles for processing by different processors whenmultiple processors are available. The process applies 44 the kernel tothe data which can be a conventional convolution operation or aspecialized operation that takes account of the characteristics of thekernels as discussed below. The results are stored 46 in the originalmatrix and the kernel is moved 48 by incrementing the pointers. If theprocess is not operating on the sides in parallel as would occur whenparallel processors are involved, the process determines whether 50 acorner has been reached and whether 52 the last corner has been reached.If the last corner has not been reached 50 the kernel is rotated 54 andapplied 44 again. When the last corner has been reached the corners mayneed to be processed 56. If the level is not the last level 58, thekernel is moved 60 down a level and level processing starts again. Ifthe last level has been reached special final center processing may needto be performed 62, the image is complete 64 and may be viewed orprinted. Because the length of each side is gradually shrinking and thecorners are moving inward the process must keep track of the processingposition as well as the level. A person of skill in the art can providethe appropriate pointers and pointer adjustments for this.

As noted above, the first level is processed first. Every pixel in thelevel can be processed independently of every other so that some degreeof parallel processing is simple to obtain, even though the processsteps discussed above process the sides sequentially. During processingalong the top row (excluding the corners), each pixel in the first levelis replaced by the convolution of the pixel area with the top kernelshown in FIG. 7A. This operation is shown in FIG. 7A for the pixel whosevalue is 5 and is located in row 3 column 4 (starting the count from toprow and left most column). To clarify the illustration, the interiorlevels and corners are not shown. The darkly bordered box is shownenclosing the pixels whose convolution coefficients are non-zero. Thus,the resulting pixel value is

((2×1)+(5×1)+(1×4)+(2×4)+(3×4)+(4×1)+(5×1))/16=2.5 or more simplybecause of the design of the kernel ((2+5+5+4)+(1+2+3)×4)/16=2.5. Thisrounds (for integer-valued pixels) to 2. The result is shown in row 3,column 4 of FIG. 7B. The results for the rest of the beginning of thatrow are shown as well as the results for the beginning of the column.All of the new pixel values are shown in a bold font.

The corner pixels can be computed by averaging the results of theconvolutions using the two relevant kernels. Thus the upper, left cornerpixel value in the first level shown here will average the result of thetop kernel for that pixel location (result is 3) and the result of theleft kernel for that location (result is 5). The average value istherefore 4. As an alternative and for a faster computation one of thekernel pixels can be arbitrarily chosen, for example always choose thetop and bottom kernel results for the corners. However, since thekernels pull information from different areas of the image the finalresult will be different though generally the difference would not benoticeable. FIG. 8 shows the overall result for the calculation atlevel 1. The pixels in level 1 (between the dark lines) have beenreplaced, all others are left alone.

After level 1 has been computed, level 2 pixels can be computed. Thelevel two pixels are shown in FIG. 9 between the dark lines. Thesepixels are replaced in exactly the same manner as for level 1. Thisprocedure repeats until one level remains. If, as is shown in FIG. 10,there are an even number of pixels in the center of region of interest,a two-by-two area of pixels will remain at the last level. Each of thesefour pixels can be treated as a corner in exactly the same way asbefore. The resulting, completely processed region of interest is shownin FIG. 10.

The final level shown in FIG. 10 will occur when the region of interestis square and has an even number of pixels in each dimension. If thearea is square but has an odd number of pixels in each dimension, thelast level will contain a single pixel as shown in FIG. 11. (FIGS. 11through 14 show only the last few levels of a larger region ofinterest.) In this case, the central pixel (originally 19) can bereplaced by the average of all four kernel operations (top, left, rightand bottom). In this case, the value of the top kernel is 19, the valueof the left kernel is 22, the value of the right kernel is 21, and thevalue of the bottom kernel is 23, for an average value of 21. The pixelvalue of 21 will thus replace the original, central value of 19. For afaster computation as an alternative the pixel for one of the kernelscan be arbitrarily selected as previously mentioned.

If the region of interest is not square, but is rectangular the finallevel will be either a pair of lines or a single line depending onwhether an even or odd number of lines or columns are included in theregion of interest. The case for a pair of lines is illustrated in FIG.12. In this case, no special procedures need to be invoked, since normaloperation will reduce the final level. In the latter case, FIG. 13, aspecial procedure, analogous to that of FIG. 11, must be used.

In the case of a remaining line for the last level, the end pixels canbe replaced with the average of the top, left or right, and bottomkernels. In the FIG. 13 example, the results are 21, 17 and 19 for thetop, left and bottom kernels respectively, so the replacement valuewould be the average of those three values, or 19. On the right side,the top, right and bottom kernels would be applied, and the average ofthose three results used. For the pixels not on the end, the top andbottom kernels would be applied and their results averaged. For example,the pixel value 29 which is second from the left in the last level ofFIG. 13 has a top kernel value of 23, and a bottom kernel result of 24.The average result is then 23.

FIG. 13 illustrates the case in which the horizontal dimension of theregion of interest is greater than the vertical, resulting in ahorizontal line for the last level. If the vertical dimension is greaterthan the horizontal, a vertical line results. This case is treated in asimilar way. The resulting end pixels are averaged from the left, rightand top or bottom kernels. The remaining pixels are the result ofaveraging the values derived from applying the left and right kernels.

The procedures described above are very computationally efficient onbinary, digital computers. No multiply or divide operations need to bedone, only shifts and adds. Tracking the location of the pixel argumentsfor the kernels is more work, but the total is much less than requiredfor a conventional convolution, for example. In addition, the specialcases FIGS. 11 through 12, only pertain to regions of interest with odddimensions. If the regions of interest are restricted to even row orcolumn sizes, the special cases do not obtain and further simplificationcan be achieved.

If an image has a lot of high frequency detail, the procedure outlinedabove can result in a perceptibly visible border around the obscuredarea. This can be reduced by using the slightly smaller kernels shown inFIGS. 14A-14D. These kernels are effectively lower powered versions ofthose previously described. By applying them in the same manner at thefirst few levels of the region of interest, a more gradual, lessperceptible edge is obtained. The number of levels for which the smallerkernels are used is image or application dependent. Note that thesmaller kernels exhibit even greater computational efficiencies than thelarger ones, since fewer adds and shifts are needed to implement them.

The iterative use of these kernels in the manner described aboveeffectively obscures image information without creating objectionableartifacts. This occurs because the method as described combines someinformation from within the obscured area with blurred information fromthe edge of the area while not pulling information from pixels beside oron the same level. As the iterations proceed, the edge information ispulled into the region of interest. Thus, the effective obscurationincreases as the iterations proceed providing a smooth transition fromthe relatively unaffected border regions to the interior of the obscuredarea. There are three simultaneous activities occurring. First, thelocal pixels are blurred as with a typical convolution in which adjacentpixels at the same level are combined; this effectively reduces theimage detail. Second, information from within the region of interest isincorporated into the obscured pixels; this reduces the perception ofartifacts within the obscured area. Third, information from the edge ofthe region of interest is moved into the obscured area, effectivelyproviding structural continuity for the image as well as covering theobjectionable image information. In addition, information from the outerreaches of the region is dragged into the center or middle.

Examination of FIG. 10 will show that the detail has been effectivelyremoved while some structure remains without creating a clear edge inthe processed area. Note that a digital representation of an image areacannot be used to provide a visually accurate representation of theactual image processing.

The present invention can be improved by applying a differently shapedregion of interest to the image along with kernels shaped to match thechange in the shape region of the region of interest. A secondsimplified artificial image 100 used to depict improved region ofinterest is shown in FIG. 15. The feature which will be obscured withinthis image is again the ball 82 at the end of the human figure's leftarm 84 on the right side of the artificial image. The area to beobscured is selected within an ellipse 102 by an operator. The ellipse102 completely surrounds the area to be obscured.

The elliptical area to be obscured can be digitally, rather thanpictorially, represented by FIG. 16A. In this figure, the background haspixel values in the 0 to 10 range, the hand has values in the 40 to 70range, and the circle or ball 82 has values in the 130 to 160 range. Thearea within the darkly bordered digitized ellipse 102 is to be obscured.To clarify the illustration pictorially, FIG. 16B adds a lightbackground to each pixel area for the hand, and a darker backgroundshade for each pixel of the circle or ball 82.

Eight separate kernels are used to obscure the region of interest eachwith a shape corresponding to the shape of the region with which it isused, thereby drawing information into the region from the appropriatedirection. The kernels are "V-shaped" recognizing that the kernels areto pull from regions exterior to the curved surface of the ellipse 102.Each kernel is used on one of the eight possible regions of interest forthe pixel configurations. Note that these kernels can also be applied toa region of arbitrary shape by merely sharpening the "V" as theassociated region curve gets sharper and broadening the "V" as theregion curve gets shallower until the flat kernels of the previouslydiscussed embodiment are produced. This will of course require fewer ormore kernels for the curve depending on its shape and may increaseprocessing overhead. The eight kernels for this particular ellipse 102can be divided into two groups, one of which comprises edge kernelreflections as illustrated in FIGS. 17A-17D, and the second of whichcomprises corner kernel reflections as illustrated in FIGS. 18A-18D.These kernels are all normalized by dividing by 16 as in the previousembodiment.

Every pixel within the area to be obscured must be replaced with a newpixel value. As in the previous embodiment, the obscuration proceedsiteratively, from the outside layers, or levels, of theregion-of-interest to the inside. The first level is composed of thepixels contiguous with the inside of the dark border and is the first tobe processed. The second level is composed of the pixels just inside thefirst level and so on. Each level is processed before the next (althoughsome computations may be done at the same time if there is no arithmeticdependency as in the previous embodiment).

Every unknown pixel with three known pixels on the line above, where onepixel is directly above and the other two are on either side of the oneabove, can be calculated using the kernel of FIG. 17A. Likewise, everyunknown pixel with three known pixels on the line to the right, whereone pixel is directly to the right and the other two are above and belowthe pixel on the right, can be calculated using the kernel of FIG. 17C.Every unknown pixel with known pixels above, to the right, anddiagonally above and to the right can be calculated using the kernel ofFIG. 18B. The corresponding situations for the upper left, and lowerright and left quadrants are similar, using the appropriately reflectedkernels. It is possible that more than one kernel may be usable. In thatcase, either kernel may be chosen. (The corner kernel is chosen here.)Alternatively, both convolutions can be calculated and the average usedas in the previous embodiment.

Note that not every pixel contiguous with a known pixel can becalculated on a given level of processing. The appropriate neighborhoodof known pixels must first exist. However, as the erosive processcontinues iteratively, every pixel will eventually be filled in.

At any one level, only the pixels whose kernel elements are combinedwith new pixels can be modified (except for the pixel of interest at thecenter of the kernel). Pixels whose kernel elements are zero areignored. New pixels (those whose new values are already determined) arethose which are not in the elliptical region of interest 102 or onesreplaced at a previous level calculation.

The various configurations for kernel application are illustrated inFIG. 19 for pixels in the upper right quadrant of the elliptical regionof interest. The pixels and their positions are indicated with squareboxes as in the previous figures. Boxes filled with an 0 represent knownor previously modified pixels. Boxes filled with an x represent pixelsthat have not yet been modified. Boxes filled with a bold r indicatepixels that have not yet been modified but that can be modified giventhe existing known and modified pixels.

Empty boxes are at the edge of the area and are ignored. As with aconvolution, a neighborhood of pixels is required for this calculation;those pixels without a defined neighborhood must be treated separately.Those pixels are not considered here and may be filled according to someother scheme or simply left with their existing value. Generally,however, this problem does not arise because the area to be obscured isnot at the very edge of the image data. Given this, every pixel in thearea to be obscured will have a neighborhood.

FIG. 20 completes the FIG. 19 example using pixel values from FIG. 16A.The r boxes have been filled using the convolution values from theappropriate kernels. The original pixel in the upper left r box location104 is 2 (from FIG. 16A). Thus, the upper left r values in FIG. 19 iscomputed using the top kernel, so that the resulting pixel value is

((6×1)+(2×1)+(3×1)+(4×4)+(5×4)+(1×4)+(2×1))/16=3.3125 or more simplybecause of the design of the kernel ((6+2+3+2)+(4+5+1)×4)/16=3.3125.This rounds (for integer-valued pixels) to 3. Where either a corner orside kernel could be used, an arbitrary choice is preferably made toapply the corner kernels.

Because the digitized ellipses are quadrant symmetric, the same kernelsare applied at the same relative points for each quadrant. This reducesthe computation required to select the appropriate kernel. (Thus eachcomputation to select a kernel supports the calculation of four newpixel values.) To apply the appropriate kernels to the current levelpixels, the following procedure of FIG. 21 can be used for a singlequadrant. FIG. 19 illustrates the quadrant in which the procedure isapplied. The processing starts 110 with positioning the kernel at thetop center or at pixel 104 of FIG. 19. The top kernel is applied 112 anda test 114 is performed to determine whether the pixel to the right iscomplete, that is, has the pixel to the right already been processed. Ifnot, the kernel is moved 116 to the right and the right pixel is againtested 118. If the right pixel is not complete then the edge ishorizontal and the top kernel is again applied 112. If the right pixelis complete the upper right kernel is applied 120 because an insidecorner has been detected. Next, the lower right pixel is tested 122 andif it is not complete, the pixel 124 is moved to the lower right. Thesystem then tests 126 the right pixel attempting to identify anothercorner or a horizontal edge. When the lower right pixel is complete(122), the system tests 128 the pixel below and if it is complete thelevel processing is finished 130. If the below pixel is not complete,the kernel is moved 132 down. Next, the lower right pixel is tested 134to look for a vertical edge. If the lower right pixel is complete avertical edge has been detected and the right kernel is applied 136. SeeFIG. 20 for the results. To start the next level the kernel is movedback to the top center. Note that this procedure can be applied to theremaining quadrants by simple rotation of the pixels tested. Forexample, if the lower left quadrant is to be processed step 112 wouldapply the right kernel and step 114 would test the pixel below. Notethat the actual convolution must be done individually for each pixel,since, even though the kernel is the same (although rotated) for each ofthe four pixels, the pixel values are not the same. FIG. 22 shows theresult of applying the appropriate kernel to one entire level of theselected area of FIG. 16A.

After each level is calculated, the next level can proceed. Every pixelin a level can be calculated at the same time or in parallel as in theprevious embodiment. If the pixels are processed or calculated inparallel, for each pixel a test is performed to see if all pixelscorresponding to the non-zero kernel elements are complete. If so, thepixel of interest is on the current level and may be calculated. If not,the pixel must be replaced at a later date. If they are calculatedsequentially, as was illustrated for the first embodiment in FIG. 2 andin FIG. 21, the pixels on the next level may be calculated if therequired complete pixels exist or have been calculated. FIGS. 23 and 24show the second and third level results of the method applied to theselected area of FIG. 16A. Note that the shape of the ellipse isconverting into a rectangle which reduces the number of kernels whichare used finally reducing to just top, bottom and side kernels.

At some stages, especially the final ones, more than one kernel will bevalid for the convolution. When this occurs, either an arbitrary choicebetween kernels may be made or the average result of all of the validkernels may be used as in the previous embodiment. This will also happenif a single pixel, row of pixels, or column of pixels remain. Forexample, if a horizontal row of unknown pixels remains, both the top andbottom kernels can be used and the results of the kernel convolutionsmay be averaged. FIG. 25 shows the final result of the method as appliedto the selected area of FIG. 16A. To improve the clarity of the example,averaging is not done and the corner kernels are chosen in preference tothe side kernels. The upper right kernel is attempted first.

All of the convolutions described above with respect to the secondembodiment are also very computationally efficient on binary, digitalcomputers. Once again no multiply or divide operations need to be done,only shifts and adds. Finding out which kernel to apply does requiremore work, as well as tracking which pixels need to be calculated. Thiscan readily be done by maintaining a template of the region of interestwhich contains values indicating the status of each pixel. Additionalwork is necessary to support averaging. All of which is within the skillof the art. The elliptical approach does require more overhead than therectangular approach, but the visual results are superior.

The iterative use of the kernels of FIGS. 17 and 18 in the mannerdescribed above effectively obscures image information without creatingobjectionable artifacts. Using an elliptical region of interest andassociated appropriately shaped kernels, while requiring slightly morework computationally than using rectangular regions of interest,provides superior results. The elliptical approach maintains all otheradvantages of the rectangular region of interest approach.

This invention provides an automatic method for the efficientobscuration of image information without creating image artifacts thatare readily perceptible to the viewer. The procedure does not requireany user interaction once the region to be obscured is defined. Theresulting image information lacks the objectionable image detail, butincorporates image structure from within and without the obscured area.The obscuration at the edge of the region is very small, while theeffective level of obscuration is larger within the interior. Thus, thearea is effectively obscured but does not create objectionablediscontinuities within the image. The approach is very computationallyefficient, requiring only additions and arithmetic shift operations. Theuse of an elliptical region of interest provides superior results to anapproach using rectangular regions of interest while maintaining all ofits advantages with only a slight increase in computational cost. Theelliptical region of interest approach described herein is superior inthat the borders created are not linear (either vertically orhorizontally) and thus are less perceptible to the human visual system.The elliptical approach is also superior in that the regions describedtend to be more natural and less artificial. The elliptical approach isalso superior in that there is a reduced tendency to create diagonalartifacts within the obscured area.

The shape of the region used for processing can be arbitrary as long asit is a closed curve and the kernels of FIGS. 17 and 18 modifiedaccording to curve sharpness can be used to process the region. Thus, itis possible for the user to outline the area to be obscured usingconventional drawing techniques and have only that limited regionobscured.

The present invention has been described with respect to a particularprocess step order of sequentially marching around the region ofinterest using appropriate kernels, detecting when all pixels of a levelhave been processed and moving in a level until all pixels have beenprocessed. It is of course possible that other approaches can be taken,such as processing in parallel all at once by testing non-zero kernelelement pixels for completeness.

The many features and advantages of the invention are apparent from thedetailed specification and, thus, it is intended by the appended claimsto cover all such features and advantages of the invention which fallwithin the true spirit and scope of the invention. Further, sincenumerous modifications and changes will readily occur to those skilledin the art, it is not desired to limit the invention to the exactconstruction and operation illustrated and described, and accordinglyall suitable modifications and equivalents may be resorted to, fallingwithin the scope of the invention.

    ______________________________________                                                Parts List                                                            ______________________________________                                                10     Camera                                                                 12     Image                                                                  14     Computer                                                               16     Display                                                                18     Operator                                                               20     Input Device                                                           22     Region                                                                 24     Obscuration Process                                                    26     Printer                                                                40-64  Process Steps                                                          80     Image                                                                  82     Ball                                                                   83     Hand                                                                   84     Arm                                                                    86     Person                                                                 88     Region                                                                 90     Pixel                                                                  100    Image                                                                  102    Region                                                                 104    Pixel                                                                  110-136                                                                              Process Steps                                                  ______________________________________                                    

What is claimed is:
 1. A method of obscuring information in an image,comprising:(a) obtaining a digital signal representative of the image;(a) designating a region of the image for obscuration; and (b) mergingfirst image signal information from outside the region with second imagesignal information from inside the region by applying at least oneconvolution kernel to the region from an outside edge of the region to acenter in discrete sequential layers, the result of each layer beingmerged with the next layer; wherein the at least one convolution kernelis rotationally symmetric about a normal to the surface of the region,and wherein the at least one convolution kernel comprises at least eightrotationally symmetric kernels.
 2. A method of obscuring information inan image, comprising:(a) obtaining a digital signal representative ofthe image; (a) designating a region of the image for obscuration; and(b) merging first image signal information from outside the region withsecond image signal information from inside the region by applying atleast one convolution kernel to the region from an outside edge of theregion to a center in discrete sequential layers, the result of eachlayer being merged with the next layer; wherein the it least oneconvolution kernel is v-shaped.
 3. A method as recited in claim 2,wherein the shape of the at least one convolution kernel gets narroweras the curve of the region gets sharper.